Database/AI/ML frameworks & serving
vLLM (multimodal embeddings): Memory corruption
Impact
Memory corruption → crash and possible RCE
Who can reach it
Unauthenticated request to an exposed OpenAI-compatible serving port carrying crafted embeddings
What to do
Upgrade to 0.11.1+. If the provider hosts the endpoint, this is provider-owned; if the tenant runs their own vLLM, it is tenant-owned and the provider can only enforce network policy
Fleet impact
How widespread
Very common - vLLM is the default open-source inference server on nearly every neocloud's serverless-inference and BYO-endpoint product
Cost to remediate
daemon-restart - upgrade to 0.11.1+ and roll the serving fleet; every replica of every model endpoint must be restarted
Why it hits the whole fleet
The Completions API torch.loads user-supplied prompt embeddings; with PyTorch 2.8's sparse-tensor checks off by default a crafted tensor causes an out-of-bounds write - network-only, unauthenticated-adjacent, on every serving replica
References
This entry is curated: imported from vendor advisories with machine assistance, not yet individually verified. Confirm against your vendor's advisory before acting, and report anything wrong.